10 Ways Machine Learning is Transforming Business Part 3

In parts 1 and 2 I covered a few machine learning use-cases such as predictive scoring and smart KPI monitoring. At this point you’re probably wondering how these insights can be passed down to the end user in a way that is both intuitive and easy to use. Before we get there I’d like to cover some more applications that are emerging for machine learning. Let’s dive in!

1.Product and Content Recommendations

In parts 1 and 2 I covered a few machine learning use-cases such as predictive scoring and smart KPI monitoring. At this point you’re probably wondering how these insights can be passed down to the end user in a way that is both intuitive and easy to use. Before we get there I’d like to cover some more applications that are emerging for machine learning. Let’s dive in!

Recommendation engines are the most pervasive and common uses of AI applications that exist. They have been integrated so seamlessly to the point that users, like yourself, are not even aware that the product they are currently viewing was served to them by a combination of big data and machine learning.

When applied to what we here at Basix are doing—we not only predictively targets leads, but also apply collaborative filtering algorithms in order to match your agents with leads based on previous successes and failures. These algorithms can stack and work together with different objectives and purposes to the benefit of the entire overall model.

2.Prevent Fraud

Traditional fraud prevention employs teams trained in looking for particular patterns that match a predetermined set of criteria. This not only results in many false positives, but it also lets many cases of fraud slip through the cracks. Additionally, detecting fraud is also a very timely endeavour and with every passing hour, the fraudsters are more likely to get away.

Machine learning can act quick enough to stop them during the act.

ML models can be trained to pry through endless data sets to detect patterns and behaviours that indicate high probability of fraudulent actions. To get them started, data from previous cases of fraud is inputted into the machine learning algorithms in order to calibrate and configure the models. When real-time data begins to flow in, algorithms like the Random Decision Forest are able to quickly build out decision trees and flag cases that would have otherwise gone undetected.

Taking it a step further, these same models are also able to proactively predict new types of fraud, and immediately catch fraudsters before they even get a chance to act. Building out a strategy that works predictively and with a greater hit rate will ultimately free up valuable human resources that can be put to work elsewhere.

Additionally, human error often gets tagged incorrectly as fraud and clogs up valuable company time and resources. Rather than accepting it as a sunk cost, machine learning algorithms are able to improve their detection rates after every iterative successful finding. This approach can also be applied to finding errors that are caused by the system itself, and correct the errors as part of standard operations.

3.IoT

According to IBM, over 90% of IoT data will remain unused. By pairing machine learning with IoT, departments from customer service all the way to engineering will be able to optimize and improve efficiencies across the board.

Consider this: using a fixed maintenance schedule cuts into profits by over-maintaining perfectly functioning equipment and by failing to timely maintain malfunctioning equipment. Machine learning is able to schedule the predictive maintenance of equipment, drastically reducing the consequences caused by overservice and underservice.

Operations-critical data like remaining lifetime of equipment and likelihood of equipment failure can be gleaned from parsing data through ML models. What this results in is less equipment downtime and more appropriate utilization of live technicians—ultimately having an impact on bottom line.

Under current methodologies, valuable data science talent is being wasted on data hygiene practices; time that could be spent on mission critical data tasks is spent scrubbing away junk data and formatting data sets. Machine learning models and automation can work in unison to format and clean data as it’s gathered so that it can be utilized in real time.

4.Truly Custom Machine Learning Tools

The current software development model seen predominantly with providers of legacy CRM is to offer support and build marginal improvements around software that was developed in the 90s. The prolonged effect of this approach is a tool that is always failing to adequately serve each user and unfortunately leads to potential revenue being left on the table.

Basix continuously optimizes models that power things like predictive outreach, sales agent allocation, UI smart prompts, and more—so that the best performing options and tactics are optimized in real time. Basix will even go so far as to know how and when to demand more from your agents while mitigating burnout.

Machine learning can act quick enough to stop them during the act.

With Basix, every user interaction and capability has been added for the sole purpose of driving more meaningful connections and everything outside of that is considered a distraction. You won’t find some ridiculous sales game screen with characters that divert agents’ attention away from tasks.

We found that with every other product you face constant challenges with configuration, deployment, adoption, and upkeep.

To give you an example of how Basix can work for you, imagine a simplified scenario with only one variable—the discovery of a particular time of day that an account picks up the phone. Our AI engine would then target more calls at that time the next day. Throw in countless other variables like email opens and clicks or sentiment towards your product, and you can quickly see the need for a machine learning model to find the best outreach process. It would take days in a traditional CRM to try to analyze and decipher all of the data, let alone act on it. Our engine finds subtle conversion signifiers so that your agents are always served the hottest follow ups, tasks, and prospects—without needing to use their intuition to guess what to do next.

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